The relevance of indigenous Psychologies for understanding human experience. Who gets to decide?

Multimodal binding as mereological co-constituency

Robots must be able to communicate naturally and efficiently, e.g., using concise referring forms like it, that, and the ⟨N’⟩. Recently researchers have started working on Referring Form Selection (RFS) machine learning algorithms but only evaluating them offline using traditional metrics like accuracy. In this work, we investigated how a cognitive status-informed RFS computational model might fare in actual human-robot interactions in a human-subjects study (N=36). Results showed improvements over a random baseline in task performance, naturalness, understandability, and mental workload. However, the model was not perceived to outperform a simple, naive, non-random baseline (constant use of indefinite noun phrases). We contribute several key research directions for further development of cognitive status-informed RFS models, the inclusion of multi-modality, and further development of testbeds.


Evaluating Cognitive Status-Informed Referring Form Selection for Human-Robot Interactions

Androids that strongly but imperfectly resemble humans in shape can elicit negative emotions in people, a phenomenon known as the "uncanny valley," which has been replicated in laboratory experiments. Recently, the accuracy of face recognition utilizing machine learning has increased, raising the question of whether machine learning can replicate the uncanny valley. Using FaceNet as a representative face recognition algorithm, we examined the similarity of face recognition to human evaluation and its replication of the uncanny valley. The results revealed a strong correlation between machine learning and human evaluation of human-like shapes. However, it is evident that only certain aspects of the uncanny valley were replicated. Furthermore, visualization of the activation maps suggests that localized regions, such as the mouth and chin, acted as the basis for judgment. These findings support the idea that human and machine learning have distinct areas of attention.

Does Machine Learning Replicate the Uncanny Valley? An Example using FaceNet

Working Memory (WM) plays a key role in natural language understanding and generation. To enable a human-like breadth and flexibility of language understanding and generation capabilities, cognitive systems for language-capable robots should feature a human-like WM system in a similarly central role. However, it is still quite unclear how robotic WM should be designed, as a variety of models of human WM have been proposed in cognitive psychology. Moreover, human reliance on WM during language production is sometimes to help the speaker rather than to help hearers. Thus, it is unclear whether different robotic WM systems might harm certain dimensions of interaction for the sake of the robot speaker's ostensible ease of cognitive processing. In this paper we demonstrate how different models of human WM can be implemented into robot cognitive architectures. Our results suggest that these models can be effective in terms of accuracy, perceived naturalness, and perceived human-likeness.

Forget About It: Entity-Level Working Memory Models for Referring Expression Generation in Robot Cognitive Architectures

Recording the neural activity of multiple and large neural populations in the brain is becoming widely available with modern recording techniques. It still remains a challenge, however, to understand how distinct and anatomically different neural populations interact with each other to control behaviour. We propose a new method to discover causal interactions between neural populations based on recurrent switching dynamical systems. We introduce an extended dynamics model that incorporates the current time-step when calculating the latent state variables. We also introduce an acyclicity constrain in learning the parameters of the model. These mechanisms enable rich causal interactions between neuronal populations to be identified from the learned model. Our model outperforms previous work on discovering interactions between neural populations in simulated datasets, without sacrificing the prediction performance of firing rates. We also apply our method on real neural recordings from two macaque monkey brains performing a behavioral task, and show that the proposed method is able to detect causal interactions between brain regions related to the different time windows of the task.

Discovering Low-Dimensional Causal Pathways between Multiple Interacting Neuronal Populations

A causal illusion occurs when people perceive a causal relationship between two events that are not contingent on each other. How this illusion varies when people reason diagnostically (i.e., in an effect-to-cause direction) is not well known. Thus, this experiment explored how diagnostic reasoning affects causal illusions. Participants learnt about a illusory cause-effect relationship in which the probability of the cause and the probability of the effect were orthogonally manipulated to be either high or low. Participants learnt either predictively (i.e., cause-to-effect) or diagnostically, and at test had to make two causal judgements that encouraged either predictive (cause-to-effect) or diagnostic (effect-to-cause) reasoning. Diagnostic reasoning at test increased the strength of the cause density bias and decreased the strength of the effect density bias. It also decreased causal ratings, but only after predictive learning. These findings highlight the promise of diagnostic reasoning as a tool to debias causal decision making.

Question Framing Modulates the Cause Density and Effect Density Biases in Causal Illusions

How responsible someone is for an outcome depends on both the causal role of their actions, and what those actions reveal about their moral character. Prior work has successfully modeled people's causal attributions and mental state inferences using planning algorithms assumed to approximate people's intuitive theory of mind about others' behavior. In this paper, we develop a unified computational framework for responsibility judgments in which the same generative planner can model both of these processes. We test our framework on a variety of animated social scenarios in two experiments. Experiment 1 features simple cases of helping and hindering. Experiment 2 features more complex interactions that require recursive reasoning, including cases where one agent affects another by merely signaling their intentions without physically acting on the world. Our model accurately captures participants' counterfactual simulations and intention inferences. Together, these two factors explain responsibility judgments.

A computational framework for responsibility judgments from counterfactual simulations and intention inferences

We investigated how verbal communication with a robot differs from talking to a human in terms of brain activity by analysing an open-source fMRI dataset. We focused on modeling conversational dynamics rather than conversation as a whole, by analysing fine-grained events, in particular turn initiation. The results indicate that turn initiation in a conversation with a human involves higher activation in auditory and visual cortex than turn initiation with a robot. Conversely, listening to the robot showed higher engagement of auditory cortex than listening to a human. We suggest that verbal and non-verbal turn-taking cues provided by the human agent engage more cognitive processing for picking up the turn. On the other hand, listening to a robot agent requires more processing than listening to a human. Both findings suggest that the accurate simulation of appropriate turn-taking cues and behaviors will help robots to establish more natural conversation dynamics and that the use of brain imaging can provide valuable objective measurements for assessing user states in human-robot interaction.

Investigating Conversational Dynamics in Human-Robot Interaction with fMRI

High-quality parent language may support preschoolers’ language development by shaping underlying neurodevelopment. Indeed, more interactive language relates to higher frontal brain activity (Romeo et al., 2018). However, it remains to be explored whether other features of parent language may also be related to brain activity. This study examined whether parental linguistic sophistication is related to children’s spontaneous high-frequency neural activity, as indexed by EEG gamma power. Transcripts of parent-child dyads (N=36, children M=4.1 years old) completing a puzzle task were analyzed for mean length of utterance (measure of grammatical complexity) and number of different words (measure of vocabulary sophistication). We found that higher parental mean length of utterance was related to higher regional EEG gamma power (F(2.61,80.78)=2.92, p=.046), suggesting that more grammatically complex parent language is linked with higher child neural maturity. Future studies should explore the directionality of this finding and further measures of parent language quality.

Sophisticated parent language is linked to high-frequency neural activity in preschoolers

People can understand images that vary in visual abstraction---from detailed illustrations to schematic icons. 
To what degree are current vision algorithms sensitive to such variation when encoding their meaning? 
We first obtained >80K human-generated sketches produced under different time limits (4s, 8s, 16s, 32s; N=5,563 participants) and AI-generated sketches (Vinker et al., 2022) produced under different ink limits (8, 16, 32 strokes) of 2,048 real-world objects spanning 128 categories from the THINGS dataset (Hebart et al.,2019).
We then evaluated how 13 state-of-the-art vision algorithms varying in architecture and training represented the meaning of these sketches, both at the category and exemplar levels, and without additional training.
We found that these models were generally sensitive to variation in visual abstraction, although some models were substantially more sensitive than others.
Together, these findings suggest promising avenues for building better models of human visual abstraction.

Evaluating machine comprehension of sketch meaning at different levels of abstraction

Recent years have seen remarkable advances in the development and use of Artificial Intelligence (AI) in image classification, driving cars, and writing scientific articles. Although AI can outperform humans in many tasks, there remain domains where humans and AI working together can outperform either working alone. For humans and AI to work together effectively, the human must trust the AI bot to the right degree (calibrated). If the human does not trust the bot sufficiently, or conversely trusts the bot more than is warranted, the human-bot team will not perform as well as they could. We report three experiments examining trust in human-AI teaming. While existing studies typically collect binary responses (to trust, or not to trust), we present a novel paradigm that quantifies trust in a bot-recommendation in a continuous fashion. These data allow better precision, and in the future the development of more refined models of human-bot trust.

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The relevance of indigenous Psychologies for understanding human experience. Who gets to decide?

Multimodal binding as mereological co-constituency

Evaluating Cognitive Status-Informed Referring Form Selection for Human-Robot Interactions

Does Machine Learning Replicate the Uncanny Valley? An Example using FaceNet

Forget About It: Entity-Level Working Memory Models for Referring Expression Generation in Robot Cognitive Architectures

Discovering Low-Dimensional Causal Pathways between Multiple Interacting Neuronal Populations

Question Framing Modulates the Cause Density and Effect Density Biases in Causal Illusions

A computational framework for responsibility judgments from counterfactual simulations and intention inferences

Investigating Conversational Dynamics in Human-Robot Interaction with fMRI

Sophisticated parent language is linked to high-frequency neural activity in preschoolers

Evaluating machine comprehension of sketch meaning at different levels of abstraction

Trust in Human-bot Teaming: Applications of the Judge Advisor System

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